This is RuBERT tiny2 model fine tuned for sentiment classification of short Russian texts. The task is a multi class classification with the following labels: Label to Russian label: Usage Dataset This model was trained on the union of the following datasets: Kaggle Russian News Dataset Linis Crowd 2015 Linis Crowd 2016 RuReviews RuSentiment An overview of the training data can be found on S. Smetanin Github repository. Download links for all Russian sentiment datasets collected by Smetanin can be found in this repository. Training Training were done in this project with this parameters: Train/validation/test splits are 80%/10%/10%. Eval results (on test split) neutral positive negative macro avg weighted avg precision 0.7 0.84 0.74 0.76 0.75 recall 0.74 0.83 0.69 0.75 0.75 f1 score 0.72 0.83 0.71 0.75 0.75 auc roc 0.85 0.95 0.91 0.9 0.9 support 5196 3831 3599 12626 12626
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